Diaptomid Thermal Limits

Site Map

coords = ctmax_data %>%
  inner_join(site_data, by = c("site", "lat", "collection_temp")) %>% 
  dplyr::select(site, long, lat, collection_temp, elevation) %>%
  drop_na(collection_temp) %>% 
  distinct()

map_data("world") %>% 
  filter(region %in% c("USA", "Canada")) %>% 
  ggplot() + 
  geom_polygon(aes(x = long, y = lat, group = group),
               fill = "grey92", colour = "grey40", linewidth = 0.1) + 
  coord_map(xlim = c(-110,-60),
            ylim = c(25, 55)) + 
  geom_point(data = coords,
             mapping = aes(x = long, y = lat),
             size = 2) +
  labs(x = "Longitude", 
       y = "Latitude",
       colour = "Elev. (m)") + 
  theme_matt() + 
  theme(legend.position = "right")

CTmax Data

temp_lat_plot = ctmax_data %>% 
  select(lat, collection_temp) %>% 
  distinct() %>% 
  ggplot(aes(x = lat, y = collection_temp)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(size = 3) + 
  labs(x = "Latitude", 
       y = "Collection Temp. (°C)") + 
  theme_matt() + 
  theme(legend.position = "right")

ctmax_temp_plot = ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = ctmax)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(aes(colour = species), 
             size = 3) + 
  labs(x = "Collection Temp. (°C)", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ctmax_lat_plot = ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = lat, y = ctmax)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(aes(colour = species), 
             size = 3) + 
  labs(x = "Latitude", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ctmax_elev_plot = ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = elevation, y = ctmax)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(aes(colour = species), 
             size = 3) +
  labs(x = "Elevation (m)", 
       y = "CTmax (°C)") +
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ggpubr::ggarrange(temp_lat_plot, ctmax_temp_plot, ctmax_lat_plot, ctmax_elev_plot, common.legend = T, legend = "right", nrow = 2, ncol = 2, labels = "AUTO")

ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = ctmax, colour = species)) + 
  facet_wrap(species~.) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point() + 
  labs(x = "Collection Temp. (°C)",
       y = "CTmax (°C)") + 
  scale_color_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "none")

ctmax_data %>% 
  filter(str_detect(species, pattern = "skisto") | 
           str_detect(species, pattern = "lepto") | 
           str_detect(species, pattern = "aglao")) %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  group_by(collection_date, species, collection_temp) %>% 
  summarise(mean_ctmax = mean(ctmax),
            ctmax_sd = sd(ctmax),
            ctmax_n = n(), 
            ctmax_se = ctmax_sd / sqrt(ctmax_n)) %>% 
  ggplot(aes(x = collection_temp, y = mean_ctmax, colour = species)) + 
  geom_smooth(method = "lm", se=F, linewidth = 2) + 
  geom_point(size = 2) + 
  geom_errorbar(aes(ymin = mean_ctmax - ctmax_se, 
                    ymax = mean_ctmax + ctmax_se),
                width = 0.3, linewidth = 1) + 
  labs(x = "Collection Temp. (°C)",
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = size, colour = species)) + 
  facet_wrap(species~.) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point() + 
  labs(x = "Collection Temp. (°C)",
       y = "Prosome Length (mm)") + 
  scale_color_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "none")

ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = egg_volume, colour = species)) + 
  facet_wrap(species~.) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point() + 
  labs(x = "Collection Temp. (°C)",
       y = "Egg Volume (mm^3)") + 
  scale_color_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "none")

ctmax_data %>% 
  select(elevation, collection_temp) %>% 
  distinct() %>% 
  ggplot(aes(x = elevation, y = collection_temp)) + 
  geom_point(size = 3) +
  labs(x = "Elevation (m)", 
       y = "Collection Temp. (°C)") + 
  theme_matt()

ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = size, y = ctmax, colour = species)) + 
  #facet_wrap(.~species) + 
  geom_point(size = 1) + 
  theme_matt() + 
  labs(x = "Length (mm)", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme(legend.position = "none")

ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = size, y = fecundity, colour = species)) + 
  facet_wrap(.~species) + 
  geom_point(size = 1) + 
  theme_matt() + 
  labs(x = "Length (mm)", 
       y = "Fecundity (# eggs)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme(legend.position = "none")

ggplot(ctmax_data, aes(x = size, y = total_egg_volume)) + 
  geom_smooth(method = "lm", formula = y ~ exp(x)) + 
  geom_point()+
  labs(x = "Prosome Length (mm)",
       y = "Total Egg Volume (mm^3)") + 
  theme_matt()

Data for just Skistodiaptomus pallidus is shown below. Point color is arranged according to latitude.

ctmax_data %>% 
  filter(species == "skistodiaptomus_pallidus") %>%
  mutate(site = fct_reorder(site, lat, .desc = T)) %>% 
  # group_by(site) %>% 
  # summarise(size = mean(size, na.rm = T), 
  #          total_egg_volume = mean(total_egg_volume, na.rm = T)) %>% 
  ggplot(aes(x = size, y = total_egg_volume)) + 
  geom_smooth(method = "lm", formula = y ~ exp(x), 
              colour = "black") + 
  geom_point(aes(colour = site))+
  scale_color_viridis_d(direction = 1, 
                        option = "D") + 
  labs(x = "Prosome Length (mm)",
       y = "Total Egg Volume (mm^3)") + 
  theme_matt() + 
  theme(legend.position = "right")

model_data = ctmax_data %>% 
  mutate("genus" = str_split_fixed(species, pattern = "_", n = 2)[,1],
         genus = tools::toTitleCase(genus),
         "doy" = yday(collection_date)) %>% 
  select(site, collection_date, doy, collection_temp, lat, elevation, species, genus, sample_id, fecundity, total_egg_volume, size, ctmax) %>% 
  filter(genus != "MH") %>%  
  mutate(total_egg_volume = if_else(is.na(total_egg_volume), 0, total_egg_volume),
         collection_temp_sc = scale(collection_temp),
         lat_sc = scale(lat), 
         elevation_sc = scale(elevation),
         tev_sc = scale(total_egg_volume)) 

ctmax_overall.model = lm(data = model_data, 
                         ctmax ~ genus + collection_temp + lat + elevation + total_egg_volume)

drop1(ctmax_overall.model, test = "F")
## Single term deletions
## 
## Model:
## ctmax ~ genus + collection_temp + lat + elevation + total_egg_volume
##                  Df Sum of Sq    RSS     AIC  F value    Pr(>F)    
## <none>                        485.97  -4.365                       
## genus             2   138.079 624.04 117.677  70.6070 < 2.2e-16 ***
## collection_temp   1   105.341 591.31  92.519 107.7332 < 2.2e-16 ***
## lat               1   113.236 599.20  99.203 115.8076 < 2.2e-16 ***
## elevation         1     6.509 492.48   0.341   6.6572   0.01016 *  
## total_egg_volume  1    21.484 507.45  15.438  21.9722 3.578e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#MuMIn::dredge(ctmax_temp.model)

car::Anova(ctmax_overall.model)
## Anova Table (Type II tests)
## 
## Response: ctmax
##                  Sum Sq  Df  F value    Pr(>F)    
## genus            138.08   2  70.6070 < 2.2e-16 ***
## collection_temp  105.34   1 107.7332 < 2.2e-16 ***
## lat              113.24   1 115.8076 < 2.2e-16 ***
## elevation          6.51   1   6.6572   0.01016 *  
## total_egg_volume  21.48   1  21.9722 3.578e-06 ***
## Residuals        485.97 497                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

performance::check_model(ctmax_overall.model)

egg.model = lm(data = model_data, 
                         ctmax ~ collection_temp + total_egg_volume + size)

performance::check_model(egg.model)

effectsize::effectsize(egg.model)
## # Standardization method: refit
## 
## Parameter        | Std. Coef. |        95% CI
## ---------------------------------------------
## (Intercept)      |   6.39e-16 | [-0.07, 0.07]
## collection temp  |       0.61 | [ 0.54, 0.68]
## total egg volume |       0.15 | [ 0.08, 0.23]
## size             |       0.37 | [ 0.30, 0.44]

emmeans::emmeans(ctmax_overall.model, specs = "genus") %>% 
  data.frame() %>% 
  mutate(genus = fct_reorder(genus, .x = emmean, .desc = T)) %>% 
  ggplot(aes(genus, y = emmean)) + 
  geom_point(size = 4) + 
  geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), 
                width = 0.2, linewidth = 1) + 
  labs(x = "") + 
  theme_matt() + 
  theme(axis.text.x = element_text(angle = 300, hjust = 0, vjust = 0.5))

ctmax_temp.model = lm(data = model_data, 
                      ctmax ~ species + collection_temp)

drop1(ctmax_temp.model, 
      scope = ~.,
      test = "F")
## Single term deletions
## 
## Model:
## ctmax ~ species + collection_temp
##                 Df Sum of Sq    RSS      AIC F value    Pr(>F)    
## <none>                       328.04 -196.438                      
## species          8    587.82 915.86  305.034  110.65 < 2.2e-16 ***
## collection_temp  1    116.01 444.05  -45.823  174.70 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

performance::check_model(ctmax_temp.model)

sp_means = emmeans::emmeans(ctmax_temp.model, "species") %>% 
  data.frame() %>% 
  drop_na() %>%  
  select(species, "ctmax" = emmean, lower.CL, upper.CL)


sp_means %>% 
  mutate(species = str_replace(species, pattern = "_", replacement = " "),
         species = str_to_sentence(species),
         species = fct_reorder(species, .x = ctmax)) %>% 
ggplot(aes(x = species, y = ctmax, colour = species)) + 
  geom_point(size = 3) + 
  geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), 
                width = 0.5) + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(axis.text = element_text(angle = 300, hjust = 0, vjust = 0.5), 
        legend.position = "none")

ctmax_data %>% 
  mutate(group_id = paste(site, species, collection_date)) %>% 
  ggplot(aes(x = fecundity, y = site, fill = site)) + 
  geom_density_ridges(bandwidth = 2,
                      jittered_points = TRUE, 
                      point_shape = 21,
                      point_size = 1,
                      point_colour = "grey30",
                      point_alpha = 0.6,
                      alpha = 0.9,
                      position = position_points_jitter(
                        height = 0.1, width = 0)) + 
  scale_fill_viridis_d(option = "D", direction = -1) + 
  theme_matt() + 
  theme(legend.position = "none")

ctmax_data %>% 
  mutate(group_id = paste(site, species, collection_date)) %>% 
  ggplot(aes(x = size, y = site, fill = site, group = group_id)) + 
  geom_density_ridges(bandwidth = 0.02,
                      jittered_points = TRUE, 
                      point_shape = 21,
                      point_size = 1,
                      point_colour = "grey30",
                      point_alpha = 0.6,
                      alpha = 0.9,
                      position = position_points_jitter(
                        height = 0.1, width = 0)) + 
  scale_fill_viridis_d(option = "D", direction = -1) + 
  theme_matt() + 
  theme(legend.position = "none")

ctmax_data %>% 
  mutate(group_id = paste(site, species, collection_date)) %>% 
  ggplot(aes(x = ctmax, y = site, fill = site, group = group_id)) + 
  geom_density_ridges(bandwidth = 0.3,
                      jittered_points = TRUE, 
                      point_shape = 21,
                      point_size = 1,
                      point_colour = "grey30",
                      point_alpha = 0.6,
                      alpha = 0.9,
                      position = position_points_jitter(
                        height = 0.1, width = 0)) + 
  scale_fill_viridis_d(option = "D", direction = -1) + 
  labs(x = "CTmax (°C)") + 
  theme_matt() + 
  theme(legend.position = "none")

F3 Data

Skistodiaptomus pallidus was collected from three sites (Centennial Park - CO, Ochsner Pond - OH, and Center Springs Pond - CT) were reared in the lab at 16°C for at least three generations. CTmax was measured for these copepods to test for genetic variation in thermal limits in this widely distributed species.

Lab reared copepods varied in size, with Centennial Park individuals ~0.1 mm longer than those from Ochsner Pond.

f3_data %>%
  ggplot(aes(x = site, y = size)) + 
  geom_boxplot() + 
  geom_point(position = position_jitter(height = 0, width = 0.1)) + 
  labs(x = "Site", 
       y = "Prosome Length (mm)") + 
  theme_matt()

f3_size.model = lme4::lmer(data = f3_data,
                      size ~ site + (1 | experiment_date))

#performance::check_model(f3_size.model)

car::Anova(f3_size.model)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: size
##       Chisq Df Pr(>Chisq)    
## site 25.398  1  4.664e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Upper thermal limit did not vary between the populations.

f3_data %>%
ggplot(aes(x = site, y = ctmax)) + 
  geom_boxplot() + 
  geom_point(position = position_jitter(height = 0, width = 0.1)) + 
  labs(x = "Site", 
       y = "CTmax (°C)") + 
  theme_matt()

f3_ctmax.model = lme4::lmer(data = f3_data, 
                      ctmax ~ site + (1|experiment_date) + (1|tube))

#performance::check_model(f3_ctmax.model)

car::Anova(f3_ctmax.model)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: ctmax
##      Chisq Df Pr(>Chisq)
## site 0.827  1     0.3631

f3_data %>% 
  group_by(experiment_date, site) %>% 
  summarise(mean_ctmax = mean(ctmax), 
            se_ctmax = sd(ctmax) / sqrt(n())) %>% 
ggplot(aes(experiment_date, y = mean_ctmax, colour = site, group = site)) + 
  geom_point(data = f3_data,
             aes(x = experiment_date, y = ctmax, colour = site),
             size = 1, alpha = 0.3,
             position = position_jitterdodge(jitter.height = 0, jitter.width = 0.05, 
                                             dodge.width = 0.3)) + 
    geom_line(linewidth = 1.3,
              position = position_dodge(width = 0.3)) + 
  geom_errorbar(aes(ymin = mean_ctmax - se_ctmax, ymax = mean_ctmax + se_ctmax),
                position = position_dodge(width = 0.3),
                width = 0.25, linewidth = 1) + 
    geom_point(size = 3,
               position = position_dodge(width = 0.3)) + 
  labs(x = "Experiment Date", 
       y = "CTmax (°C)") + 
  theme_matt() + 
  theme(legend.position = "right")

Fecundity also appears to vary between populations, even after rearing in lab for several generations, although this difference does not appear to be significant.

ggplot(f3_data, aes(x = site, y = fecundity)) + 
  geom_boxplot() + 
  geom_point(position = position_jitter(height = 0, width = 0.1)) + 
  labs(x = "Site", 
       y = "Clutch Size (eggs per female)") + 
  theme_matt()

# f3_fecund.model = glm(data = f3_data, 
#                       fecundity ~ site,
#                       family="poisson")

f3_fecund.model = lme4::glmer(data = f3_data, 
                      fecundity ~ site + (1|experiment_date),
                      family="poisson")

# performance::check_model(f3_fecund.model)

car::Anova(f3_fecund.model)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: fecundity
##       Chisq Df Pr(>Chisq)
## site 2.1596  1     0.1417

We also examined how oxygen consumption rates varied across temperatures for the two populations. Results are preliminary, but it seems like the two populations may have similar optimum temperatures.

tpc_rates %>% 
  filter(temp != 32) %>% 
  filter(!(temp > 15 & rsq < 0.7)) %>% 
  ggplot(aes(x = temp, y = rate, colour = treatment)) + 
  #facet_wrap(treatment~., nrow = 2) + 
  geom_point() + 
  geom_smooth() + 
  theme_bw()

To summarize the initial findings, Centennial Park copepods had larger body sizes but smaller clutch sizes than copepods from Ochsner Pond. CTmax and thermal optima were similar between the two populations.

High throughput size measurements

scan_sizes %>% 
  filter(sex == "female", stage == "adult") %>% 
  ggplot(aes(x = length, fill = species)) + 
  facet_wrap(site~., nrow = 3) + 
  geom_histogram(binwidth = 0.01, colour = "grey10", linewidth = 0.25) + 
  scale_fill_manual(values = skisto_cols) + 
  theme_minimal(base_size = 20) + 
  theme(panel.grid = element_blank(),
        strip.text = element_text(face = "bold"),
        legend.position = "bottom")

scan_sizes %>% 
  filter(sex == "female", stage == "adult") %>%
  ggplot(aes(x = length, y = site, fill = species)) + 
  geom_density_ridges(bandwidth = 0.02,
                      jittered_points = TRUE,
                      position = position_points_jitter(yoffset = -0.15, width = 0, height = 0.1),
                      point_alpha = 0.3, point_colour = "grey30")  + 
  labs(y = "",
       x = "Prosome Length (mm)") + 
  scale_fill_manual(values = skisto_cols) + 
  theme_minimal(base_size = 20) + 
  theme(panel.grid = element_blank(),
        strip.text = element_text(face = "bold"),
        legend.position = "bottom")

scan_sizes %>% 
  filter(sex == "female", stage == "adult") %>%
  inner_join(site_data) %>% 
  ggplot(aes(x = collection_temp, y = length)) + 
  geom_point(position = position_jitter(width = 0.08, height = 0)) + 
  theme_matt()

COI Barcoding

knitr::include_graphics("../Figures/species_prop_plot.png")

knitr::include_graphics("../Figures/clade_prop_plot.png")

knitr::include_graphics("../Figures/tree_plot.png")